Emoji recommendation system and method

ABSTRACT

A system includes a memory and at least one processor to receive text from a client computing device, the text received one character at a time, as each character of the text is received, determine a recommendation in real-time to be added to the text based on at least one of a list of rules, word embedding, an n-gram model, and a co-occurrence model, the recommendation comprising at least one of a word, a list of hashtags, a quotation, and a list of emojis, and send the recommendation to the client computing device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/684,049 filed Nov. 14, 2019 entitled “Emoji Recommendation System andMethod,” the entire contents of which is incorporated herein byreference.

BACKGROUND

Companies and businesses use social media platforms to market theirproducts and services. Although the social media networks provide theability to reach customers throughout the world, it can be incrediblydifficult to determine when, how, and what to post. In particular,social media marketers may have to determine what to post in a varietyof different languages and determine creative ways to engage potentialcustomers. Even further complicating matters, certain social mediaplatforms have strict rules about similarity between posts and mayrestrict users from posting duplicated content and/or similar posts. Incertain circumstances, a user account may be suspended or eventerminated if the user posts content that is overly similar. Very largecompanies may have an entire marketing team that can handle this task.However, this is a time consuming task and for a small business, theresimply may not be enough time to determine when, how, and what to post.In addition, for a global marketer that has to create posts in Englishwhen English is not their first language, this may be nearly impossible.

It is with these issues in mind, among others, that various aspects ofthe disclosure were conceived.

SUMMARY

According to one aspect, a system for recommending one or more emojisincludes a client computing device that sends text to a server computingdevice having a predictive linguistics engine. The predictivelinguistics engine may provide a web service that may be arepresentational state transfer (REST) service. The predictivelinguistics engine receives the text one character at a time, inreal-time, from the client computing device. The predictive linguisticsengine determines a recommendation based on the text including at leastone of a word, a quotation, a hashtag, and an emoji. The servercomputing device may send the recommendation to the client computingdevice and the client computing device may display the text and therecommendation on a display in a graphical user interface (GUI). A userof the client computing device may add the recommendation to the textand post the text to at least one social media platform.

According to an aspect, a system includes a memory and at least oneprocessor to receive text from a client computing device, the textreceived one character at a time, as each character of the text isreceived, determine a recommendation in real-time to be added to thetext based on at least one of a list of rules, word embedding, an n-grammodel, and a co-occurrence model, the recommendation comprising at leastone of a word, a list of hashtags, a quotation, and a list of emojis,and send the recommendation to the client computing device.

According to another aspect, a method includes receiving, by at leastone processor, text from a client computing device, the text receivedone character at a time, as each character of the text is received,determining, by the at least one processor, a recommendation inreal-time to be added to the text based on at least one of a list ofrules, word embedding, an n-gram model, and a co-occurrence model, therecommendation comprising at least one of a word, a list of hashtags, aquotation, and a list of emojis, and sending, by the at least oneprocessor, the recommendation to the client computing device.

According to an additional aspect, a non-transitory computer-readablestorage medium includes instructions stored thereon that, when executedby a computing device cause the computing device to perform operations,the operations including receiving text from a client computing device,the text received one character at a time, as each character of the textis received, determining a recommendation in real-time to be added tothe text based on at least one of a list of rules, word embedding, ann-gram model, and a co-occurrence model, the recommendation comprisingat least one of a word, a list of hashtags, a quotation, and a list ofemojis, and sending the recommendation to the client computing device.

These and other aspects, features, and benefits of the presentdisclosure will become apparent from the following detailed writtendescription of the preferred embodiments and aspects taken inconjunction with the following drawings, although variations andmodifications thereto may be effected without departing from the spiritand scope of the novel concepts of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate embodiments and/or aspects of thedisclosure and, together with the written description, serve to explainthe principles of the disclosure. Wherever possible, the same referencenumbers are used throughout the drawings to refer to the same or likeelements of an embodiment, and wherein:

FIG. 1 is a block diagram of a system for determining a recommendationaccording to an example embodiment.

FIG. 2 shows a block diagram of a server computing device of the systemaccording to an example embodiment.

FIG. 3 illustrates a flowchart for sending a recommendation according toan example embodiment.

FIG. 4 illustrates an example user interface displayed by a clientcomputing device according to an example embodiment.

FIG. 5 illustrates an example user interface displayed by the clientcomputing device according to an example embodiment.

FIG. 6 illustrates an example user interface displayed by the clientcomputing device according to an example embodiment.

FIG. 7 illustrates a block diagram of a computing device according to anexample embodiment.

DETAILED DESCRIPTION

Aspects of a system and method for recommending one or more emojisincludes a client computing device that sends text to a server computingdevice having a predictive linguistics engine. The predictivelinguistics engine receives the text one character at a time, inreal-time, from the client computing device. The predictive linguisticsengine determines a recommendation based on the text as it is receivedincluding at least one of a word, a quotation, a hashtag, and an emoji.The server computing device may send the recommendation to the clientcomputing device and the client computing device may display the textand the recommendation on a display in a graphical user interface (GUI).A user of the client computing device may add the recommendation to thetext and post the text to at least one social media platform.

Emojis or emoticons originated in Japan in the late 1990s and havebecome popularized throughout the world during the 2010s. An emoji is apictograph, pictorial symbol, or icon that can be presented in color andused inline in text. To a computing device, an emoji is represented as acharacter similar to text. Emojis may be represented by a computingdevice using a unique code. Emojis or emoticons originally weredeveloped using ASCII (American Standard Code for Information Exchange),which is a character encoding standing for electronic communication. Thefirst 128 codes of Unicode are the ASCII characters. The Unicodestandard defines UTF-8, UTF-16, and UTF-32 and UTF-8 has been the mostpopular encoding for the internet and the World Wide Web. UTF-8 is avariable width character encoding that is able to encode each Unicodecode point using one to four 8-bit bytes. UTF-8 is Unicode (UniversalCoded Character Set) Transformation Format 8-bit.

Emojis have been incorporated in Unicode, which has allowed them toeasily be used and standardized across different operating systems andlanguages. Unicode is the universal character encoding that ismaintained by the Unicode Consortium. Unicode is an encoding standardthat provides the basis for processing, storage, and interchange of textdata in any language in all software and information technologyprotocols. In addition, Unicode is responsible for representing allcharacters for all writing systems of the world with a unique code forevery character, in every language including English. Each Unicode codeis conventionally represented by U+ four, five, or six numbers, e.g.,U+0000, which represents the null character.

722 emoji characters were incorporated into Unicode in October 2010.Since their incorporation into Unicode in 2010, emojis have continued togrow in popularity in the United States and throughout the world. Thereare now more than 2,500 emojis incorporated into Unicode.

Each emoji represents a way to graphically represent facial expressions,emotions, common objects, places, types of weather, and animals, amongothers. As a result, an emoji can be used to easily represent a thoughtor feeling and emojis can be universally understood by people who speaka variety of different languages. They have become so popular that theFace With Tears of Joy emoji was named Word of the Year by a dictionaryin 2015. The Face With Tears of Joy emoji

has a Unicode representation of U+1F602. Because they have become sopopular, marketers use emojis in their social media posts. Studies haveshown that social media posts that include emojis have increasedengagement. However, there are thousands of emojis to choose from and itmay be difficult to determine which emoji or emojis may be best to addto a social media post. Alternatively, there may be certain emojis toavoid. One or more emojis may be selected based on context of text inthe social media post and current trending topics, among otherinformation.

The studies have analyzed social media posts on social media platformssuch as LINKEDIN, FACEBOOK, TWITTER, and INSTAGRAM, among others. Thestudies have determined that there are certain emojis that are morepopular than others. In addition, certain emojis may be determined to bethe most contextually relevant based on text in the social media post, atopic associated with the social media post, and a time associated withthe social media post, among other information.

As an example, the server computing device and the predictivelinguistics engine may provide a web application programming interface(API) that may be a Representational state transfer (REST) API thatprovides a web service to receive a request and provide a response. Therequest and the response may have a payload with a particular formatsuch as Hypertext Markup Language (HTML), Extensible Markup Language(XML), Javascript Object Notation (JSON), or another type of format. Inone example, the request may include the text and the response may bethe recommendation. The request may be the following:

request-body = { ″searchText″: “text to be compl”, ″fanOut″: 2, ″depth″:2, ″ts″: 1572520688734, ″showEmojisHashtags″: true }

The searchText may be user-entered text, fanOut may be a number ofbranches each node may have, depth may indicate a height of a treestarting from a parent node, is may represent a current timestamp, andshowEmojisHashtags may be a Boolean that enables or disablesemoji/hashtag recommendations.

As an example, the predictive linguistics engine may determine arecommendation that may be a word that is a completion of a partiallyentered word. The predictive linguistics engine may generate a networkgraph that indicates what words, hashtags, and/or emojis may co-occurwith other words and phrases. When a user has not ended a post with aspace, the predictive linguistics engine may assume that the word is notcomplete and attempt to autocomplete the word. In one example,autocompletion may be based on the Python library autocomplete. ThesearchText may be completed using a library of words that may utilizeconditional probability distributions and a Hidden Markov Model (HMM).The predictive linguistics engine may determine that the text includes asingle string and may transform the string into a list of words andnormalize each word in the string. The predictive linguistics engine mayhave a normalized list of words and build a frequency distribution thatmeasures the frequency of each word to predict a final state of apartially-entered word in addition to other related words, hashtags, andemojis.

As another example, the predictive linguistics engine may complete apartially entered hashtag. The predictive linguistics engine maycomplete the partially entered hashtag when the text includes a wordthat begins with the “#” character and does not end with a space. Thepredictive linguistics engine may utilize a hashtag autocomplete modelthat includes key-value pairs from which hashtags may be selected assuggestions. Hashtags may be found in data available in real-time fromone or more social media networks and each hashtag may be used to buildkeys in the model. As an example, if a hashtag includes n letters, theremay be n−1 keys created in the model each having the hashtag as a value.For example, if the hashtag is “#dog”, the model may appear as thefollowing:

{ ‘d’:[‘dog’], ‘do’: [‘dog’] }

As another example, the hashtag may include two words such as #racecar.The words may be joined together to create the hashtag. Keys will bebuilt based on the separated words but all the keys will have the parenthashtag as their value.

Example, {‘r’: ‘racecar’, ‘ra’: ‘racecar’, ‘rac’: ‘racecar’, ‘race’:‘racecar’, ‘c’: ‘racecar’, ‘ca.’: ‘racecar’, ‘car’: ‘racecar’,}. Thisway the hashtag ‘#racecar’ is suggested not just when the user entersthe starting letters of the hashtag but also when the user enters ‘car’.

A sentence may be completed by the predictive linguistics engine usingan n-gram model. In the model, there may be one to three gram tokensthat may be stored given a corpus of data and it would be used as a baseto predict a next word in a sentence.

The n-grams and next word occurrences may be stored in a database suchas a NoSQL database or may be stored in memory. The keys may be theN-gram and the values may be a list of top ten to twenty occurrences.The model may be used to build grammatically correct sentences. As anexample, if the predictive linguistics engine receives text such as “Didyou watch the”, the predictive linguistics engine may predict words thatmay come next in the sentence such as “game,” “funny,” and “soccer” andwords that come after each of those words in the sentence.

The predictive linguistics engine may determine a recommendation thatmay be an emoji for the text including one or more user entered word,emoji, or hashtag, among other content. The predictive linguisticsengine may use one or more of a list of rules, word embedding, an n-grammodel, and a co-occurrence model. In one example, each emoji in the listof available emojis are Unicode emojis that may have an icon or image,an emoji name, and one or more relatable keywords.

As an example, word embedding may be based on one or more libraries thatare used for natural language processing, including, but not limited tospaCy. spaCy can be used to build a natural language understandingsystem for the predictive linguistics engine that can use rules as wellas machine learning. spaCy may perform tokenization, part-of speech(POS) tagging, dependency parsing, lemmatization, sentence boundarydetection (SBD), named entity recognition (NER), entity linking (EL),similarity, text classification, rule-based matching, training, andserialization, among others. Additionally, spaCy provides support for anumber of languages including English.

If an emoji is recommended based on text that is entered that includesone or more words, it may be recommended using one or more rules. As anexample, the word ‘announcement’ may have a list of one or morerecommended emojis that are related to ‘announcement’: [‘

’, ‘

’, ‘

’, ‘

’]. A dictionary may map each word to a list of one or more emojis. Inaddition, a number of recommended emojis may be based on a fanout level.

If an emoji is recommended based on word embedding, it may be based onuser entered text and emoji captions and by determining a similarityscore calculated based on word vectors between the user entered text andthe emoji captions. An emoji may be recommended when a similarity scoreis above a particular threshold. A number of recommended emojis may bebased on the fanout level.

If an emoji is recommended based on an n-gram model, the model maydetermine one or more recommended emojis based on the user entered textand occurrence of n-grams in the n-gram model. A number of recommendedemojis may be based on the fanout level.

An emoji may be recommended based on one or more emojis in the userentered text using one or more rules that include a dictionary that ismapped from one emoji to one or more related emojis. Example: {‘

’: [‘

’, ‘

’, ‘

’]}. The dictionary may include an emoji as the key and a list of emojisas its value.

An emoji may be recommended based on another user entered emoji usingword embedding. Similarity scores are computed between emoji captions(keywords) and the name of the user-typed emoji using word vectors. Thescore has to be above the specified threshold for being considered forrecommendations. Stopwords may be exempted from being considered foremoji recommendations.

An emoji may be recommended based on another user entered emoji using aco-occurrence model that may include millions of social media posts. Inone example, the millions of social media posts may be scraped usingApify or another scraping system. The data may be used to train theco-occurrence model and include a mapping from emoji to emoji. A numberof recommended emojis may be based on the fanout level.

An emoji may be recommended based on a user entered hashtag using a listof one or more rules. Emoji recommendations using rules may include adictionary mapped between hashtag to emoji. Example: {‘#celebration’: [‘

’, ‘

’, ‘

’]}.

An emoji may be recommended based on a user entered hashtag using wordembedding. Similarity scores may be determined between emoji captions(keywords) and user entered hashtags using word vectors. This score hasto be above the specified threshold for being considered intorecommendations. Stopwords may be exempted from being considered foremoji recommendations.

An emoji may be recommended based on a user entered hashtag using aco-occurrence model that may include millions of social media posts. Thedata may be used to train the co-occurrence model and include a mappingfrom hashtag to emojis. Emoji keys may include hashtag values that mayoccur when the emoji has been used. A number of recommended emojis maybe based on the fanout level.

A hashtag may be recommended based on user entered text that may haveone or more words based on one or more rules. Example: {‘morning’:[‘#fresh’, ‘#sunny’]}. The dictionary will contain a word as the key anda list of hashtags as its value. The number of hashtags recommended maybe dependent on the fanout level.

A hashtag may be recommended based on user entered text including one ormore words using an n-gram model that may be a hashtag n-gram model. Themodel may recommend one or more hashtags based on the user entered textby checking for occurrence of n-grams in the model. The number ofhashtags recommended may be dependent on the fanout level.

A hashtag may be recommended based on user entered text including one ormore emojis using one or more rules. Hashtag recommendations using rulesinclude one or more hashtags from a dictionary that are mapped toemojis. Example: {‘

’: [‘#celebration’, ‘#success’]}. The dictionary may contain an emoji asthe key and a list of hashtags as its value.

A hashtag may be recommended based on user entered text including one ormore emojis using a co-occurrence model that may include millions ofsocial media posts. The data may be used to train the hashtagco-occurrence model and include a mapping between hashtag to emoji.Hashtag keys may include emoji values that occurred when the hashtag wasused in posts. A number of recommended hashtags may be dependent on thefanout level.

A hashtag may be recommended based on user entered text including one ormore hashtags using one or more rules. Hashtags may be selected from adictionary mapped between hashtag to hashtag. Example: {‘#morning’:[‘#fresh’, ‘#sunny’]}. The dictionary will contain a hashtag as the keyand a list of hashtags as its value. The number of recommended hashtagsmay be dependent on the fanout level.

An image may be recommended based on user entered text including one ormore words that may be user entered or auto-completed. The images may berecommended from a library of images by comparing the one or more wordsin the user entered text with names of each image. The results may beused to recommend one or more images for the user entered text. As anexample, the images may be from libraries such as the Unsplash APIlibrary and/or the PyUnsplash library, among others.

The emoji recommendation system may include a memory and at least oneprocessor to receive text from a client computing device, the textreceived one character at a time, and as each character of the text isreceived, determine a recommendation in real-time to be added to thetext based on at least one of a list of rules, word embedding, an n-grammodel, and a co-occurrence model, the recommendation comprising at leastone of a word, a list of hashtags, a quotation, and a list of emojis,and send the recommendation to the client computing device.

FIG. 1 shows a block diagram of a computing system comprising an emojirecommendation system 100 according to an example embodiment. The emojirecommendation system 100 includes at least one client device 102 thatis in communication with at least one server computing device 104 via acommunication network 108. The at least one server computing device 104may have an application or at least one component of an applicationshown as a predictive linguistics engine 106.

The at least one client computing device 102 is configured to receivedata from and/or transmit data to the at least one server computingdevice 104 through the communication network 108. Although the at leastone client device 102 is shown as a single computing device, it iscontemplated that the at least one client computing device 102 mayinclude multiple computing devices.

The communication network 108 can be the Internet, an intranet, oranother wired or wireless communication network. For example, thecommunication network 108 may include a Mobile Communications (GSM)network, a code division multiple access (CDMA) network, 3^(rd)Generation Partnership Project (GPP) network, an Internet Protocol (IP)network, a wireless application protocol (WAP) network, a WiFi network,a Bluetooth network, a satellite communications network, or an IEEE802.11 standards network, as well as various communications thereof.Other conventional and/or later developed wired and wireless networksmay also be used.

The at least one client computing device 102 includes at least oneprocessor to process data and memory to store data. The processorprocesses communications, builds communications, retrieves data frommemory, and stores data to memory. The processor and the memory arehardware. The memory may include volatile and/or non-volatile memory,e.g., a computer-readable storage medium such as a cache, random accessmemory (RAM), read only memory (ROM), flash memory, or other memory tostore data and/or computer-readable executable instructions such as aportion or component of the predictive linguistics engine 106. Inaddition, the at least one client computing device 102 further includesat least one communications interface to transmit and receivecommunications, messages, and/or signals.

The at least one server computing device 104 includes at least oneprocessor to process data and memory to store data. The processorprocesses communications, builds communications, retrieves data frommemory, and stores data to memory. The processor and the memory arehardware. The memory may include volatile and/or non-volatile memory,e.g., a computer-readable storage medium such as a cache, random accessmemory (RAM), read only memory (ROM), flash memory, or other memory tostore data and/or computer-readable executable instructions such as aportion or a component of the predictive linguistics engine 106. Inaddition, the at least one server computing device 104 further includesat least one communications interface to transmit and receivecommunications, messages, and/or signals.

The at least one client computing device 102 can be a laptop computer, asmartphone, a personal digital assistant, a tablet computer, a standardpersonal computer, or another processing device. The at least one clientcomputing device 102 may include a display, such as a computer monitor,for displaying data and/or graphical user interfaces. The at least oneclient computing device 102 may also include a Global Positioning System(GPS) hardware device for determining a particular location of theclient computing device 102, an input device, such as a camera, akeyboard or a pointing device (e.g., a mouse, trackball, pen, or touchscreen) to enter data into or interact with graphical and/or other typesof user interfaces. In an exemplary embodiment, the display and theinput device may be incorporated together as a touch screen of thesmartphone or tablet computer.

The at least one client computing device 102 may display on the displaya graphical user interface (or GUI). The graphical user interface may beprovided by the predictive linguistics engine 106. The graphical userinterface enables a user of the at least one client computing device 102to interact with the predictive linguistics engine 106 and create asocial media post that includes text and content that may include one ormore words, one or more hashtags, one or more emojis, one or morequotations, and one or more images, among other content.

The predictive linguistics engine 106 may be a component of anapplication and/or service executable by the at least one servercomputing device 104. For example, the predictive linguistics engine 106may be a single unit of deployable executable code or a plurality ofunits of deployable executable code. According to one aspect, thepredictive linguistics engine 106 may include one component that may bea web application, a native application, and/or a mobile application(e.g., an app) downloaded from a digital distribution applicationplatform that allows users to browse and download applications developedwith mobile software development kits (SDKs) including the App Store andGOOGLE PLAY®, among others.

The emoji recommendation system 100 may also include a relationaldatabase management system (RDBMS) or another type of databasemanagement system such as a NoSQL database system that stores andcommunicates data from at least one database 110. The data stored in theat least one database 110 may be associated with the list of rules, wordembedding, the n-gram model, and the co-occurrence models as discussedabove that are used to determine the recommendation including words,hashtags, emojis, and images, among other recommendations. As anexample, the database 110 may include one or more tables or datastructures that may be organized to store the information associatedwith the database 110.

FIG. 2 illustrates a block diagram of the server computing device 104according to an example embodiment. The server computing device 104includes at least one processor 202 and computer readable media (CRM)204 in memory on which the predictive linguistics engine 106 or otheruser interface or application is stored. The computer readable media mayinclude volatile media, nonvolatile media, removable media,non-removable media, and/or another available medium that can beaccessed by the processor. By way of example and not limitation, thecomputer readable media comprises computer storage media andcommunication media. Computer storage media includes non-transitorystorage memory, volatile media, nonvolatile media, removable media,and/or non-removable media implemented in a method or technology forstorage of information, such as computer/machine-readable/executableinstructions, data structures, program modules, or other data.Communication media may embody computer/machine-readable/executableinstructions, data structures, program modules, or other data andinclude an information delivery media or system, both of which arehardware.

The predictive linguistics engine 106 may include a text receiver module206 for receiving text from the client computing device 102. The textreceiver module 206 may receive the text from the client computingdevice 102 one character at a time in real-time. The text receivermodule 206 may receive each character of text and determine a Unicodevalue for each value of text as it is received. In addition, the textreceiver module 206 may parse the text and determine whether eachcharacter is associated with at least one other character such that itis determined to be a word. If the text receiver module 206 finds aspace then the text receiver module 206 may determine that a number ofcharacters may represent a word. In addition, if the text receivermodule 206 finds a hashtag character, the text receiver module 206 maydetermine that a hashtag is being received. In addition, if the textreceiver module 206 finds a string of characters such as “http://”, thenthe text receiver module 206 may determine that the string of charactersis associated with a uniform resource locator (URL) or web address.

The predictive linguistics engine 106 may include a word recommendationmodule 208 that may determine at least one word based on the text fromthe client computing device 102. As an example, the word may be based onthe text. The text may be a portion or subset of the word as determinedby the word recommendation module 208. If a user inputs text such as“do”, the word recommendation module 208 may determine at least one wordsuch as “dog” or “done.” As noted above, the word recommendation module208 also may be used to complete a sentence or phrase that may be basedon one or more words that are currently entered by the user. In oneexample, the word recommendation module 208 may be implemented using aHidden Markov Model (HMM).

The word recommendation module 208 may receive a string of text thatrepresents a large collection of English-understandable text and maytransform the string into a list of words (e.g., n-grams of word length)and may normalize each word in the list of words, e.g., modify “The” to“the”. Once there is a normalized list of words, the word recommendationmodule 208 may build a frequency distribution to measure the frequencyof each word in the string of text. Using this frequency distributionand the probability that one word may follow another in the string, itis possible to predict a final state of a word in progress found in thestring.

The word recommendation module 208 may complete a sentence of the atleast one word in the text from the client computing device 102 using ann-gram model. The model may include one to three or even more gramtokens given a corpus of data and use this as a base to predict aprobability of a next word in the text. If the n-grams are stored in aNoSQL database in database 110 or in memory of the server computingdevice 104, the keys may be represented by the N-gram and values may bea list of top ten or twenty occurrences of words and/or phrases thatfollow.

As an example, possible N-grams for an example sentence “This is my car”may be the following. 1-gram: “this”, “is”, “my”, “car”. 2-gram: “Thisis”, “is my”, “my car”. 3-gram: “This is my”, “is my car”.

The predictive linguistics engine 106 may include a hashtagrecommendation module 210 that may determine at least one hashtag basedon the text from the client computing device 102. The hashtag may bedetermined based on one or more words in the text, one or more hashtagsin the text, and one or more emojis in the text. As an example, thehashtag may be based on the text and may begin with a “#” character,which is a metadata tag that is used on social media platforms and thatallows a user to tag their post such that others can find the post usingthe hashtag by searching for a particular hashtag. It is believed that ahashtag was first used on a social media platform in 2007 and they haveexploded in popularity since their first use. The text may be a portionor subset of the hashtag as determined by the hashtag recommendationmodule 210. If the user inputs text such as “#do”, the hashtagrecommendation module 210 may determine at least one hashtag such as“#dog” or “#done.” As another example, the user may input an emoji andthe hashtag recommendation module 210 may recommend at least onehashtag. As another example, the user may input one or more words andthe hashtag recommendation module 210 may recommend at least onehashtag. As another example, the user may input a word, an emoji, and ahashtag and the hashtag recommendation module 210 may recommend at leastone hashtag. The hashtag recommendation module 210 may use at least oneof a list of rules, word embedding, an n-gram model, and a co-occurrencemodel.

The predictive linguistics engine 106 may include a quote recommendationmodule 212 that may determine at least one quotation based on the textfrom the client computing device 102. The predictive linguistics engine106 may determine that the text includes a uniform resource locator(URL) or web address. The URL may reference a blog post, an article, oranother social media post that may have one or more quotations. Thequote recommendation module 212 may parse the blog post, article, or thesocial media post to find the one or more quotations and allow a user toview the one or more quotations. As an example, the quotation may be“You miss 100% of the shots you don't take.—Wayne Gretzky—MichaelScott.” The quote recommendation module 212 may parse content associatedwith the URL and find each set of matching quotation marks to identifyeach quotation to recommend.

The predictive linguistics engine 106 may include an emojirecommendation module 214 that may determine at least one emoji based onthe text from the client computing device 102. The emoji may bedetermined based on one or more words in the text, one or more hashtagsin the text, and one or more emojis in the text. As an example, the textmay be “Wow—this product is hot! Order today!” The emoji recommendationmodule 214 may recommend the fire emoji and the star-struck emoji byparsing the one or more words in the text. As another example, the usermay input an emoji and the emoji recommendation module 214 may recommendat least one emoji. As another example, the user may input a word, anemoji, and a hashtag and the emoji recommendation module 214 mayrecommend at least one emoji. The emoji recommendation module 214 mayuse at least one of a list of rules, word embedding, an n-gram model,and a co-occurrence model to provide the recommendation.

In addition, the predictive linguistics engine 106 includes a userinterface module 216 for displaying the user interface on the display.As an example, the user interface module 216 generates a native and/orweb-based graphical user interface (GUI) that accepts input and providesoutput viewed by users of the client computing device 102. The clientcomputing device 102 may provide realtime automatically and dynamicallyrefreshed information such as text associated with a social media post.The user interface module 216 may send data to other modules of thepredictive linguistics engine 106 of the server computing device 104,and retrieve data from other modules of the predictive linguisticsengine 106 of the server computing device 104 asynchronously withoutinterfering with the display and behavior of the user interfacedisplayed by the client computing device 102.

FIG. 3 illustrates a flowchart of a process 300 for sending arecommendation, according to an example embodiment. In step 302, theclient computing device 102 receives text from a user and sends the textas each character is received to the server computing device 104. Thetext may be provided by an input device such as a keyboard, a mouse, amicrophone, or another input device. In step 304, the predictivelinguistics engine 106 of the server computing device 104 may parse thetext and determine a recommendation comprising at least one of a word, aquotation, a hashtag, and an emoji. For example, each rule in the listof rules may have a recommended emoji for a list of words, at least oneword in the list of words in the text. As an example, if the textincludes a URL, the predictive linguistics engine 106 may parse contentavailable at the URL and determine one or more quotations and recommenda quotation found in an article or blog post at the URL.

In step 306, the server computing device 104 sends the recommendation tothe client computing device 102. In step 308, the client computingdevice 102 receives the recommendation from the server computing device104 and displays the recommendation on the display for the user. In step310, the user may select the recommendation and the recommendation maybe added to the text.

When the recommendation is added to the text, the text is resent back tothe server computing device 104. In step 312, the predictive linguisticsengine 106 may parse the text and determine a new recommendation basedon the changes to the text including the recommendation that is nowpresent in the text. The new recommendation may comprise at least one ofa word, a quotation, a hashtag, and an emoji. This process maycontinually occur as changes are made to the text until the user deletesthe text, saves the text as a draft post, or adds the text to a post ina library of social media posts.

As an example, the predictive linguistics engine 106 may send agraphical user interface (GUI) to the client computing device 102 anddisplay the GUI on a display of the client computing device. The GUI mayshow the text and the recommendation, among other information. Theclient computing device 102 may display the text in a text box userinterface element, display the word in the text box user interfaceelement after the text, display the list of hashtags in a hashtag userinterface element, display the quotation in a quotation user interfaceelement, and display the list of emojis in an emoji user interfaceelement. In addition, the GUI may have a number that indicates a numberof possible remaining characters for the post. The predictivelinguistics engine 106 may receive a selection of an add to library userinterface element on the GUI and add the text to a post to be publishedusing at least one social media platform.

FIG. 4 shows an example user interface of the predictive linguisticsengine 106 displayed by the client computing device 102 according to anexample embodiment. As shown in FIG. 4, the user of the client computingdevice 102 may provide text 402 as shown in a text entry box userinterface element 404. The text 402 is “Testing out an awesome post” inaddition to two emojis including a heart emoji and a fire emoji. As theuser enters each character of the text 402, the client computing device102 sends the text to the server computing device 104. As each characteris received by the server computing device 104, the predictivelinguistics engine 106 of the server computing device 104 maycontinually parse the text and continually determine a recommendationcomprising at least one of a word, a quotation, a hashtag, and an emoji.As shown in FIG. 4, a quotation recommendation may be provided in aquotes user interface element 406. A hashtag recommendation may beprovided in a hashtags user interface element 408. An emojirecommendation may be provided in an emoji user interface element 410.

FIG. 4 shows a number of recommended emojis. If the user selects one ormore of the emojis, they may be added to the text 402 and are displayedin the text entry box user interface element 404. In addition, as theuser enters the text in the text entry box user interface element 404,the predictive linguistics engine 106 may determine a word that the useris currently typing and send an autocompleted version of the word to theclient computing device 102. The portion of the word not entered by theuser may be displayed in a different color than the text already enteredin the text entry box user interface element. As an example, if the usertypes “do” that may appear as black text. The predictive linguisticsengine 106 may determine that the text is a partially completed wordsuch as “dog.” The “g” may be displayed as grey in the text entry boxuser interface element. Thus, the “do” may appear in black and the “g”may be grey. In another example, the predictive linguistics engine 106may determine an autocompletion for a phrase or a sentence that the usermay be typing.

FIG. 5 shows an example user interface of the predictive linguisticsengine 106 displayed by the client computing device 102 according to anexample embodiment. As shown in FIG. 5, the user of the client computingdevice 102 may provide text 502 as shown in the text entry box userinterface element 504. The text 502 is “Testing out this quotegenerator” and a URL. The URL shown in the text 502 is an article fromentrepreneur.com that is associated with stress. The user may copy andpaste the URL into the text entry box user interface element 504. Thepredictive linguistics engine 106 of the server computing device 104 mayparse the text and determine a recommendation comprising at least one ofa word, a quotation, a hashtag, and an emoji.

The predictive linguistics engine 106 may determine that the URL is inthe text 502 and parse content found at the URL. The content may includeone or more quotations from the article. As shown in FIG. 5, the clientcomputing device 102 may display a quotation in the quotes userinterface element 506 including “Deciding what's important and what'snot is key to reducing stress and improving productivity—and yes, thiswill help with your looming deadlines.” If the user selects thequotation from the quotes user interface element 506, the quotation maybe added to the text 502. The quotation shown in the quotes userinterface element 506 is from the article and content at the URL. Inaddition, the client computing device 102 may display one or morehashtags in the hashtag user interface element 508. As shown in FIG. 5,the one or more hashtags are URL related based on text found in thecontent of the URL including #stress. When the user selects one of thehashtags 507 shown in the hashtag user interface element 508, thehashtag is added to the text 502. The user also may modify the list ofone or more hashtags shown by selecting the “URL Related” user interfaceelement and may display trending hashtags based on real-time dataobtained from one or more social media platforms. In addition, thepredictive linguistics engine 106 may determine one or more emojisrelated to the text including the content at the URL and display the oneor more emojis in the emoji user interface element 510.

FIG. 6 shows an example user interface of the predictive linguisticsengine displayed by the client computing device 102 according to anexample embodiment. As shown in FIG. 6, the user of the client computingdevice 102 may provide text 602 as shown in the text entry box userinterface element 604. The text 602 includes a URL and “The Patriotslost to the Ravens last night—wow”. The user may copy and paste the URLinto the text entry box user interface element 604. The predictivelinguistics engine 106 of the server computing device 104 may parse thetext and determine a recommendation comprising at least one of a word, aquotation, a hashtag, and an emoji. The predictive linguistics engine106 may determine that the URL is in the text 602 and parse contentfound at the URL. The content may include one or more quotations fromthe article. As shown in FIG. 6, the client computing device 102 maydisplay a quotation in the quotes user interface element 606. As shownin FIG. 6, the quotation is “Entering Sunday, New England had won 13straight games, including the playoffs.” The quotation shown in thequotes user interface element 606 is from the article and content at theURL. If the user selects the quotation from the quotes user interfaceelement 606, the quotation may be added to the text 602. The user couldalso select a user interface element 607 that when selected causesanother quotation to be displayed in the quotes user interface element606. As shown in FIG. 6, the user interface element 607 is depicted asan arrow that when selected cycles through one or more quotes from thecontent found at the URL. In addition, the client computing device 102may display one or more hashtags in the hashtag user interface element608. As shown in FIG. 6, the one or more hashtags are URL relatedincluding ##BALTIMORERAVENS. When the user selects one of the hashtags609 shown in the hashtag user interface element 608, the hashtag 609 isadded to the text 602.

When the user is done with their post, the user may select one of theuser interface elements shown in FIG. 6 including a “Save Draft” userinterface element 610 and an “Add to Library” user interface element612. When the user selects the “Save Draft” user interface element 610,the post may be saved for later. When the user selects the “Add toLibrary” user interface element 612, the post may be scheduled to beposted to one or more social media platforms including, but not limitedto, TWITTER, FACEBOOK, LINKEDIN, and INSTAGRAM.

FIG. 7 illustrates an example computing system 700 that may implementvarious systems, such as the client computing device 102 and the servercomputing device 104, and the methods discussed herein, such as process300. A general purpose computer system 700 is capable of executing acomputer program product to execute a computer process. Data and programfiles may be input to the computer system 700, which reads the files andexecutes the programs therein such as the predictive linguistics engine106. Some of the elements of a general purpose computer system 700 areshown in FIG. 7 wherein a processor 702 is shown having an input/output(I/O) section 704, a central processing unit (CPU) 706, and a memorysection 708. There may be one or more processors 702, such that theprocessor 702 of the computer system 700 comprises a singlecentral-processing unit 706, or a plurality of processing units,commonly referred to as a parallel processing environment. The computersystem 700 may be a conventional computer, a server, a distributedcomputer, or any other type of computer, such as one or more externalcomputers made available via a cloud computing architecture. Thepresently described technology is optionally implemented in softwaredevices loaded in memory 708, stored on a configured DVD/CD-ROM 710 orstorage unit 712, and/or communicated via a wired or wireless networklink 714, thereby transforming the computer system 700 in FIG. 7 to aspecial purpose machine for implementing the described operations.

The memory section 708 may be volatile media, nonvolatile media,removable media, non-removable media, and/or other media or mediums thatcan be accessed by a general purpose or special purpose computingdevice. For example, the memory section 708 may include non-transitorycomputer storage media and communication media. Non-transitory computerstorage media further may include volatile, nonvolatile, removable,and/or non-removable media implemented in a method or technology for thestorage (and retrieval) of information, such ascomputer/machine-readable/executable instructions, data and datastructures, engines, program modules, and/or other data. Communicationmedia may, for example, embody computer/machine-readable/executable,data structures, program modules, algorithms, and/or other data. Thecommunication media may also include an information delivery technology.The communication media may include wired and/or wireless connectionsand technologies and be used to transmit and/or receive wired and/orwireless communications.

The I/O section 704 is connected to one or more user-interface devices(e.g., a keyboard 716 and a display unit 718), a disc storage unit 712,and a disc drive unit 720. Generally, the disc drive unit 720 is aDVD/CD-ROM drive unit capable of reading the DVD/CD-ROM medium 710,which typically contains programs and data 722. Computer programproducts containing mechanisms to effectuate the systems and methods inaccordance with the presently described technology may reside in thememory section 708, on a disc storage unit 712, on the DVD/CD-ROM medium710 of the computer system 700, or on external storage devices madeavailable via a cloud computing architecture with such computer programproducts, including one or more database management products, web serverproducts, application server products, and/or other additional softwarecomponents. Alternatively, a disc drive unit 720 may be replaced orsupplemented by another storage medium drive unit. The network adapter724 is capable of connecting the computer system 700 to a network viathe network link 714, through which the computer system can receiveinstructions and data. Examples of such systems include personalcomputers, Intel or PowerPC-based computing systems, AMD-based computingsystems, ARM-based computing systems, and other systems running aWindows-based, a UNIX-based, or other operating system. It should beunderstood that computing systems may also embody devices such asPersonal Digital Assistants (PDAs), mobile phones, tablets or slates,multimedia consoles, gaming consoles, set top boxes, etc.

When used in a LAN-networking environment, the computer system 700 isconnected (by wired connection and/or wirelessly) to a local networkthrough the network interface or adapter 724, which is one type ofcommunications device. When used in a WAN-networking environment, thecomputer system 700 typically includes a modem, a network adapter, orany other type of communications device for establishing communicationsover the wide area network. In a networked environment, program modulesdepicted relative to the computer system 700 or portions thereof, may bestored in a remote memory storage device. It is appreciated that thenetwork connections shown are examples of communications devices for andother means of establishing a communications link between the computersmay be used.

In an example implementation, source code executed by the clientcomputing device 102, source code executed by the server computingdevice 104, a plurality of internal and external databases, sourcedatabases, and/or cached data on servers are stored in memory of theclient computing device 102, memory of the server computing device 104,or other storage systems, such as the disk storage unit 712 or theDVD/CD-ROM medium 710, and/or other external storage devices madeavailable and accessible via a network architecture. The source codeexecuted by the client computing device 102 and the server computingdevice 104 may be embodied by instructions stored on such storagesystems and executed by the processor 702.

Some or all of the operations described herein may be performed by theprocessor 702, which is hardware. Further, local computing systems,remote data sources and/or services, and other associated logicrepresent firmware, hardware, and/or software configured to controloperations of the emoji recommendation system 100 and/or othercomponents. Such services may be implemented using a general purposecomputer and specialized software (such as a server executing servicesoftware), a special purpose computing system and specialized software(such as a mobile device or network appliance executing servicesoftware), or other computing configurations. In addition, one or morefunctionalities disclosed herein may be generated by the processor 702and a user may interact with a Graphical User Interface (GUI) using oneor more user-interface devices (e.g., the keyboard 716, the display unit718, and other user-interface devices in communication with the I/Osection 704) with some of the data in use directly coming from onlinesources and data stores. The system set forth in FIG. 7 is but onepossible example of a computer system that may be employed or beconfigured in accordance with aspects of the present disclosure.

In the present disclosure, the methods disclosed may be implemented assets of instructions or software readable by a device. Further, it isunderstood that the specific order or hierarchy of steps in the methodsdisclosed are instances of example approaches. Based upon designpreferences, it is understood that the specific order or hierarchy ofsteps in the method can be rearranged while remaining within thedisclosed subject matter. The accompanying method claims presentelements of the various steps in a sample order, and are not necessarilymeant to be limited to the specific order or hierarchy presented.

The described disclosure may be provided as a computer program product,or software, that may include a non-transitory machine-readable mediumhaving stored thereon executable instructions, which may be used toprogram a computer system (or other electronic devices) to perform aprocess according to the present disclosure. A non-transitorymachine-readable medium includes any mechanism for storing informationin a form (e.g., software, processing application) readable by a machine(e.g., a computer). The non-transitory machine-readable medium mayinclude, but is not limited to, magnetic storage medium, optical storagemedium (e.g., CD-ROM); magneto-optical storage medium, read only memory(ROM); random access memory (RAM); erasable programmable memory (e.g.,EPROM and EEPROM); flash memory; or other types of medium suitable forstoring electronic executable instructions.

The description above includes example systems, methods, techniques,instruction sequences, and/or computer program products that embodytechniques of the present disclosure. However, it is understood that thedescribed disclosure may be practiced without these specific details.

It is believed that the present disclosure and many of its attendantadvantages will be understood by the foregoing description, and it willbe apparent that various changes may be made in the form, constructionand arrangement of the components without departing from the disclosedsubject matter or without sacrificing all of its material advantages.The form described is merely explanatory, and it is the intention of thefollowing claims to encompass and include such changes.

While the present disclosure has been described with reference tovarious embodiments, it will be understood that these embodiments areillustrative and that the scope of the disclosure is not limited tothem. Many variations, modifications, additions, and improvements arepossible. More generally, embodiments in accordance with the presentdisclosure have been described in the context of particularimplementations. Functionality may be separated or combined in blocksdifferently in various embodiments of the disclosure or described withdifferent terminology. These and other variations, modifications,additions, and improvements may fall within the scope of the disclosureas defined in the claims that follow.

What is claimed is:
 1. A system comprising: a memory; and at least oneprocessor to: receive text one character at a time as part of a payloadhaving a particular format by a web application programming interface(API) provided by a predictive linguistics engine, the payload havingthe text, a fanout level, and a depth value; as each character of thetext is received, determine, by the predictive linguistics engine, arecommendation in real-time to be added to the text having a similarityscore above a particular threshold based on at least one of a list ofrules, word embedding, an n-gram model, and a co-occurrence model, therecommendation comprising at least one emoji; and send, by thepredictive linguistics engine, the recommendation.
 2. The system ofclaim 1, the at least one processor further to determine the at leastone emoji using the list of rules, each rule in the list of rules havinga recommended emoji for a list of words, and at least one word in thelist of words in the text.
 3. The system of claim 1, the at least oneprocessor further to determine the at least one emoji using a dictionarythat maps each word in a list of words to at least one emoji.
 4. Thesystem of claim 1, the at least one processor further to determine theat least one emoji using the word embedding comprising determining thesimilarity score calculated using word vectors based on the text and anemoji caption for each emoji of the at least one emoji.
 5. The system ofclaim 1, the at least one processor further to determine the at leastone emoji using the n-gram model comprising determining the at least oneemoji based on the text and occurrence of n-grams in the n-gram model.6. The system of claim 1, the at least one processor further todetermine the at least one emoji using the co-occurrence modelcomprising training the co-occurrence model using a plurality of socialmedia posts that maps the at least one emoji to at least one otherrelated emoji.
 7. The system of claim 1, the at least one processorfurther to determine the at least one emoji using a dictionary that mapsthe at least one emoji to at least one other related emoji.
 8. Thesystem of claim 1, the at least one processor further to determine thatthe text includes a uniform resource location (URL), parse contentavailable at the URL, and determine the recommendation based on thecontent available at the URL.
 9. The system of claim 1, wherein the textis received from a client computing device and the recommendation issent to the client computing device.
 10. The system of claim 1, the atleast one processor further to determine the at least one emoji usingthe word embedding comprising determining the similarity scorecalculated using word vectors based on the text and a name for eachemoji of the at least one emoji.
 11. The system of claim 1, wherein theat least one emoji is selected from a list of available emojis that havean icon or image, an emoji name, and one or more keywords.
 12. Thesystem of claim 1, wherein the at least one emoji comprises a Unicodeemoji.
 13. The system of claim 1, the at least one processor further todetermine the at least one emoji by selecting the at least one emojifrom a list of available emojis.
 14. The system of claim 13, the atleast one processor further to determine the at least one emoji byeliminating emojis to avoid from the list of available emojis.
 15. Thesystem of claim 1, the at least one processor further to determine theat least one emoji based a dictionary mapped between a hashtag in thetext to at least one other related emoji.
 16. The system of claim 1,wherein the text comprises at least one of one or more user enteredwords, one or more emojis, and one or more hashtags.
 17. The system ofclaim 1, wherein the predictive linguistics engine exempts stopwordsfrom consideration when determining the recommendation.
 18. The systemof claim 1, wherein the recommendation is added to the text as at leastone first emoji and the text and the recommendation is received by thepredictive linguistics engine in a new payload, the at least oneprocessor further to determine a new recommendation in real-time to beadded to the text and the recommendation having a similarity score abovea particular threshold based on at least one of the list of rules, theword embedding, the n-gram model, and the co-occurrence model, the newrecommendation comprising at least one second emoji, and send, by thepredictive linguistics engine, the new recommendation to a clientcomputing device.
 19. A method comprising: receiving, by at least oneprocessor, text one character at a time as part of a payload having aparticular format by a web application programming interface (API)provided by a predictive linguistics engine, the payload having thetext, a fanout level, and a depth value; as each character of the textis received, determining by the predictive linguistics engine by the atleast one processor, a recommendation in real-time to be added to thetext having a similarity score above a particular threshold based on atleast one of a list of rules, word embedding, an n-gram model, and aco-occurrence model, the recommendation comprising at least one emoji;and sending, by the predictive linguistics engine by the at least oneprocessor, the recommendation.
 20. A non-transitory computer-readablestorage medium, having instructions stored thereon that, when executedby a computing device cause the computing device to perform operations,the operations comprising: receiving text one character at a time aspart of a payload having a particular format by a web applicationprogramming interface (API) provided by a predictive linguistics engine,the payload having the text, a fanout level, and a depth value; as eachcharacter of the text is received, determining, by the predictivelinguistics engine, a recommendation in real-time to be added to thetext having a similarity score above a particular threshold based on atleast one of a list of rules, word embedding, an n-gram model, and aco-occurrence model, the recommendation comprising at least one emoji;and sending, by the predictive linguistics engine, the recommendation.